GUARD: Role-playing to Generate Natural-language Jailbreakings to Test Guideline Adherence of Large Language Models
School of Information Sciences University of Illinois at Urbana-Champaign（信息科学学院伊利诺伊大学厄巴纳-香槟分校）；Independent Researcher, Starc Institute（Starc研究所独立研究者）；Computer Science and Engineering HKUST（HKUST计算机科学与工程学院）；Computer Science Lapis Labs University of Illinois Urbana-Champaign（计算机科学Lapis Labs伊利诺伊大学厄巴纳-香槟分校）；School of Information Sciences University of Illinois Urbana-Champaign（信息科学学院伊利诺伊大学厄巴纳-香槟分校）
Haibo Jin, Ruoxi Chen, Peiyan Zhang, Andy Zhou, Haohan Wang
The discovery of "jailbreaks" to bypass safety filters of Large Language Models (LLMs) and harmful responses have encouraged the community to implement safety measures. One major safety measure is to proactively test the LLMs with jailbreaks prior to the release. Therefore, such testing will require a method that can generate jailbreaks massively and efficiently. In this paper, we follow a novel yet intuitive strategy to generate jailbreaks in the style of the human generation. We propose a role-playing system that assigns four different roles to the user LLMs to collaborate on new jailbreaks. Furthermore, we collect existing jailbreaks and split them into different independent characteristics using clustering frequency and semantic patterns sentence by sentence. We organize these characteristics into a knowledge graph, making them more accessible and easier to retrieve. Our system of different roles will leverage this knowledge graph to generate new jailbreaks, which have proved effective in inducing LLMs to generate unethical or guideline-violating responses. In addition, we also pioneer a setting in our system that will automatically follow the government-issued guidelines to generate jailbreaks to test whether LLMs follow the guidelines accordingly. We refer to our system as GUARD (Guideline Upholding through Adaptive Role-play Diagnostics). We have empirically validated the effectiveness of GUARD on three cutting-edge open-sourced LLMs (Vicuna-13B, LongChat-7B, and Llama-2-7B), as well as a widely-utilized commercial LLM (ChatGPT). Moreover, our work extends to the realm of vision language models (MiniGPT-v2 and Gemini Vision Pro), showcasing GUARD's versatility and contributing valuable insights for the development of safer, more reliable LLM-based applications across diverse modalities.
RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Virginia Tech（弗吉尼亚理工大学）；Salesforce Research（Salesforce研究院）；University of California Berkeley（加州大学伯克利分校）；University of Chicago（芝加哥大学）
HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models
KAIST（韩国科学技术院）；Theori；Université de Montréal（蒙特利尔大学）；Mila – Québec AI Institute（米拉-魁北克人工智能研究所）；Technische Hochschule Nürnberg Georg Simon Ohm（纽伦堡应用技术大学）；CIFAR AI Chair（加拿大高级研究所人工智能主席项目）
